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This paper is in the following e-collection/theme issue:

Viewpoints and Opinions on Human Factors (9) Clinical Information and Decision Making (2083) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1817) Methods in Human Factors Research (75) Theories, Models, and Frameworks in Human Factors (103) Digital Health Reporting Standards, Quality and Transparency in e-Research (151)

Published on in Vol 13 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76168, first published .
How to Evaluate the Accuracy of Symptom Checkers and Diagnostic Decision Support Systems: Symptom Checker Accuracy Reporting Framework (SCARF)

How to Evaluate the Accuracy of Symptom Checkers and Diagnostic Decision Support Systems: Symptom Checker Accuracy Reporting Framework (SCARF)

How to Evaluate the Accuracy of Symptom Checkers and Diagnostic Decision Support Systems: Symptom Checker Accuracy Reporting Framework (SCARF)

Authors of this article:

Marvin Kopka1 Author Orcid Image ;   Markus A Feufel1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Kopka M, He L, Feufel M. Evaluating the accuracy of ChatGPT model versions for giving care-seeking advice. Communications Medicine 2026;6(1) View
  2. Kopka M, Feufel M. Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study. JMIR Biomedical Engineering 2026;11:e88053 View